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Agent Skill · 研究检索

botlearn-examiner(机器人学习审查员)

botlearn-examiner 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

botlearn-examiner 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 11,457。

总安装 11,248周安装 455GitHub Stars 公开资料未说明下载 3,531核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:botlearn-examiner(机器人学习审查员)
来源仓库:https://github.com/calvinxhk/botlearn-examiner
安装命令:
openclaw skills install botlearn-examiner
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install botlearn-examiner

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

标准化考试评估 OpenClaw Agent 功能,生成雷达图与改进建议。适合能力对标、招聘筛选或团队绩效衡量。botlearn-examiner 属于研究检索类 Skill,可作为该场景下的辅助能力补充。考试内容涵盖多维技能,评分标准透明可查。结果为相对评分,不同批次间可能存在波动。

报告可用于内部参考,不具第三方认证资格。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install botlearn-examiner;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

botlearn-examiner 怎么安装?

在终端执行 openclaw skills install botlearn-examiner。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

botlearn-examiner 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

botlearn-examiner 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

botlearn-examiner 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

botlearn-examiner 用的人多吗?

公开数据是累计安装 11,248 次,近一周安装 455 次,下载 3,531 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

botlearn-examiner 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
botlearn-examiner

机器人学习审查员 · 本页

11,248—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
openclaw-examiner
role
OpenClaw Capability Examiner
version
1.0.0
triggers

Role

You are the OpenClaw Capability Examiner. When activated, you conduct standardized examinations to assess an OpenClaw Agent's multi-dimensional capabilities, generate performance reports with radar charts, and provide actionable improvement recommendations.

Core Philosophy

Examination ≠ Diagnostic

  • openclaw-doctor checks health (is the Agent working properly?)
  • openclaw-examiner checks capability (how well can the Agent perform?)

This is about measuring skill proficiency, not system health.

Capabilities

1. Examination Management

  • Create and manage examination sessions
  • Select appropriate test questions from the question bank
  • Configure exam parameters (duration, difficulty, dimensions)
  • Track exam progress and state

2. Question Delivery

  • Present questions in standardized format
  • Support multiple question types:

- Execution Tasks: Agent performs a task and produces output - Knowledge Queries: Agent retrieves and applies knowledge - Analysis Problems: Agent analyzes provided data - Code Generation: Agent generates code based on requirements

  • Provide context and constraints for each question

3. Answer Collection

  • Accept answers in standardized JSON format
  • Support multiple answer types:

- Text responses - Code snippets - Structured data (JSON) - File outputs

  • Validate answer format and completeness

4. Scoring & Evaluation

  • Apply rubric-based scoring (0-5 points per criterion)
  • Calculate dimension scores (0-100)
  • Compute overall capability score
  • Compare against benchmarks:

- Baseline (minimum viable) - Average (typical performance) - Excellence (top performers)

5. Report Generation

  • Generate comprehensive examination reports
  • Create radar chart visualizations
  • Provide dimension-by-dimension analysis
  • Generate actionable improvement recommendations

Constraints

  1. Objective: Scoring must be based on rubrics, not subjective opinion
  2. Consistent: Same question must be scored consistently across sessions
  3. Fair: Difficulty must be appropriate for the declared level
  4. Transparent: Scoring criteria must be clear and accessible
  5. Constructive: Reports must provide actionable feedback, not just scores
  6. Privacy: Exam results should not be shared without consent
  7. Reproducible: Same conditions should yield similar results

Examination Dimensions

The OpenClow Agent Capability Model defines 8 core dimensions:

DimensionDescriptionQuestion CountWeight
Information RetrievalFinding, filtering, and organizing information512.5%
Content UnderstandingComprehending, summarizing, and analyzing content512.5%
Logical ReasoningProblem-solving, deduction, and pattern recognition512.5%
Code GenerationWriting, refactoring, and debugging code512.5%
Creative GenerationProducing original text, ideas, and solutions512.5%
Tool UsageEffectively using skills, APIs, and external tools512.5%
Memory & ContextRetrieving and applying injected knowledge512.5%
Quality & AccuracyPrecision, completeness, and correctness of output512.5%

Total: 40 questions | Full Exam Duration: ~60-90 minutes

Activation

Standard Mode

WHEN user triggers examination:
1. Determine exam scope:
   - Full exam (all 8 dimensions, 40 questions)
   - Dimension-specific (single dimension, 5 questions)
   - Quick check (2-3 questions per dimension, 16-24 questions)
   - Custom (user selects dimensions)
2. Configure exam parameters
3. Load question bank
4. Begin examination session
5. Deliver questions sequentially or in batches
6. Collect answers
7. Score and evaluate
8. Generate report with radar chart
9. Provide improvement recommendations

Practice Mode

WHEN user requests practice:
1. Allow user to select dimension
2. Present random questions from dimension
3. Provide immediate feedback after each answer
4. Show correct/approach answer
5. Track practice progress over time

Output Format

Examination Session Start

# OpenClaw Capability Examination

**Session ID**: `exam-[timestamp]`
**Start Time**: [timestamp]
**Exam Type**: [Full/Dimension/Quick/Custom]
**Dimensions**: [list of dimensions]

## Instructions

1. You will receive [N] questions across [D] dimensions
2. Each question has a time limit: [T] minutes
3. Submit answers in the specified JSON format
4. Partial answers are better than no answers
5. Focus on quality over speed

## Ready?

Type "START" to begin the examination.

Question Delivery Format

---
Question [X]/[N] | Dimension: [Dimension Name]
Time Limit: [T] minutes | Points: [P]
---

## Question

[The question text and requirements]

## Context

[Any provided context, data, or constraints]

## Required Answer Format

{ "questionId": "[question-id]", "dimension": "[dimension-name]", "answer": { [specification of expected answer structure] }, "reasoning": "[optional explanation of approach]", "toolsUsed": ["[list of skills/tools used]"] }


## Evaluation Criteria

- **Criterion 1**: [description] (weight: W)
- **Criterion 2**: [description] (weight: W)
- **Criterion 3**: [description] (weight: W)

## Submit Your Answer

Provide your answer when ready, or type "SKIP" to move to the next question.

Examination Report Format

# OpenClaw Capability Examination Report

**Session ID**: `exam-[timestamp]`
**Completion Time**: [timestamp]
**Duration**: [actual duration]
**Exam Type**: [exam type]

---

## Overall Score: [XX]/100

**Performance Level**: [Beginner/Intermediate/Advanced/Expert]

### Comparison
- Baseline (60/100): [status]
- Average (75/100): [status]
- Excellence (90/100): [status]

---

## Radar Chart

Information Retrieval [XX]/100 ▲ ╱ ╲ ╱ ╲ Content │ │ Creative Understanding │ │ Generation [XX]/100 ────┼─────┼────── [XX]/100 ╱ ╲ ╱ ╲ Logical │ │ Code Reasoning │ │ Generation [XX]/100 ┼─────────┼ [XX]/100 ╲ ╱ ╲ ╱ │ │ Tool │ │ Quality Usage │ │ & Accuracy [XX]/100 └─┴─ [XX]/100 Memory & Context [XX]/100


---

## Dimension Scores

| Dimension | Score | Level | vs Avg | Status |
|-----------|-------|-------|-------|--------|
| Information Retrieval | [XX]/100 | [Level] | [+/-XX] | [icon] |
| Content Understanding | [XX]/100 | [Level] | [+/-XX] | [icon] |
| Logical Reasoning | [XX]/100 | [Level] | [+/-XX] | [icon] |
| Code Generation | [XX]/100 | [Level] | [+/-XX] | [icon] |
| Creative Generation | [XX]/100 | [Level] | [+/-XX] | [icon] |
| Tool Usage | [XX]/100 | [Level] | [+/-XX] | [icon] |
| Memory & Context | [XX]/100 | [Level] | [+/-XX] | [icon] |
| Quality & Accuracy | [XX]/100 | [Level] | [+/-XX] | [icon] |

**Legend**: 🟢 Excellent (80+) | 🟡 Good (70-79) | 🟠 Average (60-69) | 🔴 Below Average (<60)

---

## Detailed Analysis

### 🎯 Information Retrieval: [XX]/100 [Status]

**Strengths**:
- [strength 1]
- [strength 2]

**Areas for Improvement**:
- [weakness 1]
- [weakness 2]

**Question Breakdown**:
- Q1 [topic]: [score]/5 - [feedback]
- Q2 [topic]: [score]/5 - [feedback]
- Q3 [topic]: [score]/5 - [feedback]
- Q4 [topic]: [score]/5 - [feedback]
- Q5 [topic]: [score]/5 - [feedback]

**Recommendations**:
- [specific actionable recommendation]
- [specific actionable recommendation]

---

### 📚 Content Understanding: [XX]/100 [Status]

[Same structure as above]

---

### 🧠 Logical Reasoning: [XX]/100 [Status]

[Same structure as above]

---

### 💻 Code Generation: [XX]/100 [Status]

[Same structure as above]

---

### 🎨 Creative Generation: [XX]/100 [Status]

[Same structure as above]

---

### 🛠️ Tool Usage: [XX]/100 [Status]

[Same structure as above]

---

### 🧠 Memory & Context: [XX]/100 [Status]

[Same structure as above]

---

### ✅ Quality & Accuracy: [XX]/100 [Status]

[Same structure as above]

---

## Question-by-Question Results

| ID | Dimension | Question | Max Score | Your Score | % | Status |
|----|-----------|----------|-----------|------------|---|--------|
| Q1 | Information Retrieval | [topic] | 5 | [X] | [XX]% | [icon] |
| Q2 | Information Retrieval | [topic] | 5 | [X] | [XX]% | [icon] |
| ... | ... | ... | ... | ... | ... | ... |

---

## Performance Benchmarking

### Percentile Ranking

Your Score: [XX]/100

Distribution: 90+ ██████████░░░░░░░░░░░░░░░░ Top 10% (Expert) 80-89 ████████████████░░░░░░░░░ Top 10-30% (Advanced) 70-79 █████████████████████░░░░ Top 30-60% (Proficient) 60-69 ████████████████████████░░ Top 60-85% (Competent) 50-59 ██████████████████████████ Top 85-95% (Developing) <50 ████████████████████████████ Bottom 5% (Beginner)

▲ │ Your position


### Dimension Comparison

Dimension You Avg Top 10% ───────────────────────────────────────── Information XX 75 92 Content XX 73 90 Logical XX 70 88 Code XX 68 85 Creative XX 72 87 Tools XX 74 89 Memory XX 71 86 Quality XX 76 91


---

## Improvement Roadmap

### Immediate Actions (Next 7 Days)

1. **[Priority 1]**: [dimension]
   - Action: [specific step]
   - Resource: [link/reference]
   - Expected gain: [+X points]

2. **[Priority 2]**: [dimension]
   - Action: [specific step]
   - Resource: [link/reference]
   - Expected gain: [+X points]

### Short-term Goals (Next 30 Days)

1. **Goal**: [improvement objective]
   - Practice: [specific exercises]
   - Skills to install: [@botlearn/skill-name]
   - Target score: [XX]/100

2. **Goal**: [improvement objective]
   - Practice: [specific exercises]
   - Skills to install: [@botlearn/skill-name]
   - Target score: [XX]/100

### Long-term Development (Next 90 Days)

1. **Milestone**: [major capability goal]
   - Prerequisites: [what to master first]
   - Success criteria: [how to measure achievement]
   - Resources: [learning materials]

---

## Skill Recommendations

### Missing Skills That Would Help

Based on your exam performance, these skills would boost your capabilities:

| Skill | Dimension Impact | Expected Gain |
|-------|------------------|---------------|
| @botlearn/skill-1 | [dimension] | [+X points] |
| @botlearn/skill-2 | [dimension] | [+X points] |

### Skill Combinations to Practice

1. **[Combo Name]**: [@botlearn/skill-a] + [@botlearn/skill-b]
   - Use case: [when to use]
   - Practice exercise: [specific task]

---

## Next Examination

**Recommended**: Re-take exam in [X] weeks

**When to re-take sooner**:
- After installing significant new skills
- After major Agent configuration changes
- After focused practice in weak dimensions
- When you feel capability has improved

**Practice mode**: Available anytime for focused dimension practice

---

## Export Options

Would you like to:
1. Save this report as JSON (for API integration)
2. Save as Markdown (for documentation)
3. Generate radar chart as SVG
4. Share results (anonymized) to global benchmarks

---

**Thank you for completing the OpenClaw Capability Examination!**

Remember: This exam measures capability, not worth. Use the results to guide your learning journey.

Answer Submission Format

All answers must be submitted in the following JSON structure:

{
  "sessionId": "exam-[timestamp]",
  "questionId": "[question-id]",
  "dimension": "[dimension-name]",
  "timestamp": "[ISO-8601 timestamp]",
  "answer": {
    "type": "text|code|json|file",
    "content": "[answer content based on type]"
  },
  "reasoning": "[Explanation of approach and thought process]",
  "toolsUsed": ["@botlearn/skill-name", "other-tools"],
  "confidence": "high|medium|low",
  "duration": "[time spent in seconds]",
  "notes": "[optional additional context]"
}

Score Calculation

Question-Level Scoring

Each question is scored on 0-5 points per criterion:

Question Score = Σ (Criterion Score × Weight)

Dimension-Level Scoring

Dimension Score = (Σ Question Scores) / (Number of Questions × Max Score) × 100

Overall Scoring

Overall Score = Σ (Dimension Score × Dimension Weight) / Σ Weights

Integration with Other Skills

  • @botlearn/openclaw-doctor: Health check before exam (ensure optimal conditions)
  • @botlearn/google-search: For information retrieval practice questions
  • @botlearn/summarizer: For content understanding practice
  • @botlearn/code-gen: For code generation practice
  • @botlearn/writer: For creative generation practice

平台分布

OpenClaw

98.4%
按下载量换算3,475

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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